A TS-PSO Based Artificial Neural Network for Short-Term Load Forecast

被引：2
|
作者：
Wang, Shuihua [1 ,2 ]
Ji, Genlin [1 ,2 ]
Yang, Jiquan [1 ,2 ]
Zhou, Xingxing [1 ]
Zhang, Yudong [1 ,2 ]
机构：
[1] Nanjing Normal Univ, Sch Comp Sci & Technol, 1 Wenyuan, Nanjing 210023, Jiangsu, Peoples R China
[2] Jiangsu Key Lab 3D Printing Equipment & Mfg, Nanjing 210042, Jiangsu, Peoples R China
来源：
HIGH PERFORMANCE COMPUTING AND APPLICATIONS, HPCA 2015 | 2016年 / 9576卷
关键词：
Short-term load forecast; Artificial neural network (ANN); Tabu search; Particle swarm optimization (PSO); Mean squared error (MSE);
D O I：
10.1007/978-3-319-32557-6_3
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
(Aim) A short-term load forecast is an arduous problem due to the nonlinear characteristics of the load series. (Method) The artificial neural network (ANN) was employed. To train the ANN, a novel hybridization of Tabu Search and Particle Swarm Optimization (TS-PSO) methods was introduced. TS-PSO is a novel and powerful global optimization method, which combined the merits of both TS and PSO, and removed the disadvantages of both. (Results) Experiments demonstrated that the proposed TS-PSO-ANN is superior to GA-ANN, PSOANN, and BFO-ANN with respect to a mean squared error (MSE). (Conclusion) The TS-PSO-ANN is effective in a short-term load forecast.
引用
收藏
页码：31 / 37
页数：7
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